Cross-Visual Style Change Detection for Remote Sensing Images via Representation Consistency Deep Supervised Learning

Cross-Visual Style Change Detection for Remote Sensing Images via Representation Consistency Deep Supervised Learning

Change detection techniques, which extract different regions of interest from bi-temporal remote sensing images, play a crucial role in various fields such as environmental protection, damage assessment, and urban planning. However, visual style interferences stemming from varying acquisition times,...

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Bibliographic Details
Main Authors: Jinjiang Wei, Kaimin Sun, Wenzhuo Li, Wangbin Li, Song Gao, Shunxia Miao, Yingjiao Tan, Wei Cui, Yu Duan
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Remote Sensing
Subjects:
change detection
deep supervision
representation consistency
remote sensing
error validation
Online Access:https://www.mdpi.com/2072-4292/17/5/798
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